SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates
Open accessAbstract
The lack of reliable methods for identifying descriptors---the sets of parameters capturing the underlying mechanisms of a material's property---is one of the key factors hindering efficient materials development. Here, we propose a systematic approach for discovering descriptors for materials' properties, within the framework of compressed-sensing-based dimensionality reduction. The sure independence screening and sparsifying operator (SISSO) tackles immense and correlated features spaces, and converges to the optimal solution from a combination of features relevant to the materials' property of interest. In addition, SISSO gives stable results also with small training sets. The methodology is benchmarked with the quantitative prediction of the ground-state enthalpies of octet binary materials (using ab initio data) and applied to the showcase example of predicting the metal/insulator classification of binaries (with experimental data). Accurate, predictive models are found in both cases. For the metal-insulator classification model, the predictive capability is tested beyond the training data: It rediscovers the available pressure-induced insulator-to-metal transitions and it allows for the prediction of yet unknown transition candidates, ripe for experimental validation. As a step forward with respect to previous model-identification methods, SISSO can become an effective tool for automatic materials development.
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The accuracy of the regression model provides an objective metric to optimize the hyperparameters that define the representation, and even to determine the most effective non-linear function of features that reproduces the target properties.
“The accuracy of the regression model also provides an objective metric to optimize the hyperparameters that define the representation 107 , and even to determine the most effective non-linear function of features that reproduces the target properties 108 .”
From Unsupervised machine learning in atomistic simulations, between predictions and understanding (2019) · cites this paper as [108]
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- Authors
- Ouyang, R., Curtarolo, S., Ahmetcik, E., Scheffler, M., & Ghiringhelli, L. M.
- Affiliations
- Fritz Haber Institute of the Max Planck Society (DE)
- Venue
- Physical Review Materials
- Publisher
- American Physical Society (APS)
- Published
- 2018-08-07
- Volume
- 2
- Issue
- 8
- ISSN
- 2475-9953 (ISSN-L)
- OpenAlex
- W2810294304
- Cited by
- 944
- References
- 50
- Citations by year
- 2026: 181 · 2025: 217 · 2024: 161 · 2023: 108 · 2022: 118 · 2021: 62 · 2020: 62 · 2019: 31 · 2018: 4
- FWCI
- 14.83
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Physical Review Materials (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Horizon 2020 (676580, 740233)
- U.S. Department of Defense
- Office of Naval Research (N00014-13-1-0635, N00014-11-1-0136, N00014-15-1-2863)
- Alexander von Humboldt-Stiftung
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Materials Science › Materials Chemistry › Material Selection and Properties
- Keywords
- SISSO, compressed sensing, dimensionality reduction, material descriptors, descriptor selection, metal-insulator transition, pressure-induced phase transition, materials property prediction, ab initio data, limited training data
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